Data quality in a distributed learning environment
Vast amounts of data to improve cancer treatment decisions
The FAIR guiding principles for data management and stewardship (FAIR = Findable, Accessible, Interoperable, Re-usable) have received significant attention, but little is known about how scientific protocols and workflows can be aligned with these principles.
Here, we propose to develop the FAIR Workbench that will enable researchers to explore, consume, and produce FAIR data in a reliable and efficient manner, to publish and reuse (computational) workflows, and to define and share scientific protocols as workflow templates. Such technology is urgently needed to address emerging concerns about the non-reproducibility of scientific research.
We focus our attention on different types of workflows, including computational drug repositioning to illustrate fully computational workflows and related systematic reviews to illustrate mixed (manual/computational) workflows. We explore the development of FAIR-powered workflows to overcome existing impediments to reproducible research, including poorly published data, incomplete workflow descriptions, limited ability to perform meta-analyses, and an overall lack of reproducibility.
We will demonstrate our technology in our use case of finding new drugs and targets for cardiovascular diseases, such as heart disease and stroke. As workflows lie at the heart of data science research, our work has broad applicability beyond the Life Science top sector.